Image Preprocessing and Cropping for Machine Learning Training

The above code snippet iterates through a range of numbers from 21 to 41, processing three images for each number: an input image, a mask image, and a label image. It opens and reads these images using the Python Imaging Library (PIL) and converts them into NumPy arrays.

The code then calls a function named 'strided_crop', passing in these image arrays along with additional arguments like 'input_dim' and 'stride'. This function likely performs a cropping operation on the images, potentially using the provided dimensions and stride values for data augmentation. It may also apply preprocessing or augmentation techniques to the images.

Overall, this code snippet is part of a larger image processing pipeline designed for training a machine learning model, specifically a segmentation model. The input images, masks, and labels are used to train the model, which can then be used to automatically segment new images.